Structured Bayesian Latent Factor Models with Meta-data
thesisposted on 08.07.2019, 22:54 by HE ZHAO
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
In the era of big data, huge amounts of data are being generated from the internet, social networks, phone apps, and so on, which creates high demand for powerful and efficient data analysis techniques. In areas such as collaborative filtering, text analysis, graph analysis, and bioinformatics, a large proportion of such data can be formulated into discrete matrices. This research focuses on developing structured Bayesian latent factor models with meta-data for analysing discrete data in the above areas. Compared with state-of-the-art methods, the proposed approaches have achieved not only better modelling performance and efficiency, but also preferable interpretability for intuitively understanding those data.